Computational Engineering (COE)
COE 301 Introduction to Computer Programming 3 Hours
3 Lecture Hours 0 Lab Hours
Basic computer programming concepts applied to engineering computations. Development of structured solutions to engineering and mathematical problems and an understanding of coding practices. Programming in MATLAB and C++. Aerospace Engineering 301 and Computational Engineering 301 may not both be counted. Three lecture hours a week for one semester.
Grading: Student Option
Repeatable for credit: No
Academic Level: Lower Division
COE 311K Engineering Computation 3 Hours
3 Lecture Hours 0 Lab Hours
Fundamental numerical methods and software tools used in engineering computation. Subjects include linear systems of equations, matrix computations, nonlinear equations, least squares approximations, interpolation, numerical integration and numerical solution of differential equations. Only one of the following may be counted: Aerospace Engineering 211K, 311, Biomedical Engineering 313L, Computational Engineering 211K, 311K. Three lecture hours a week for one semester.
Pre/Corequisites: Aerospace Engineering 301 (or 201) or Computational Engineering 301 with a grade of at least C-; and credit with a grade of at least C- or registration for Mathematics 427J or 427K.
Grading: Student Option
Repeatable for credit: No
Academic Level: Lower Division
COE 119, 219, 319, 419, 519, 619, 719, 819, 919 Topics in Computational Engineering 1-9 Hours
1,2,3,4,5,6,7,8,9 Lecture Hours 0 Lab Hours
For each semester hour of credit earned, one lecture hour a week for one semester.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Lower Division
COE 119S, 219S, 319S, 419S, 519S, 619S, 719S, 819S, 919S Topics in Computational Engineering 1-9 Hours
0 Lecture Hours 0 Lab Hours
This course is used to record credit the student earns while enrolled at another institution in a program administered by the University's Study Abroad Office. Credit is recorded as assigned by the study abroad adviser in the Department of Aerospace Engineering and Engineering Mechanics. University credit is awarded for work in an exchange program; it may be counted as coursework taken in residence. Transfer credit is awarded for work in an affiliated studies program.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Lower Division
COE 321K Computational Methods for Structural Analysis 3 Hours
3 Lecture Hours 0 Lab Hours
Matrix structural analysis of systems that can be idealized as being comprised of axial bar elements, beam elements, and frame elements. Notion of element-by-element assembly of the governing system of algebraic equations. A systematic introduction to (and use of) the fundamental idea of a weak statement of a boundary value problem, particularly as concerns the numerical treatment/approximation of such problems. Interpretation of the weak-statement in terms of the principle of virtual work. Galerkin's method as applied to structural analysis. An introduction to the classical Galerkin finite element method with application to structures and plane elasticity. Energy principles and their utility in solving problems in solid mechanics, as well as their connection to the finite element method. Aerospace Engineering 321K and Computational Engineering 321K may not both be counted. Three lecture hours a week for one semester, with discussion hours to be arranged.
Pre/Corequisites: Computational Engineering 311K (or Aerospace Engineering 211K or Computational Engineering 211K); and Engineering Mechanics 319 with a grade of at least C- in each.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
COE 322 Scientific Computation 3 Hours
3 Lecture Hours 0 Lab Hours
Explores the basic tools needed for developing scientific computing software. These include advanced programming languages (e.g. C, C++, python), object oriented programming and data structures. Subjects may include abstract data types; creation, initialization, and destruction of objects; class hierarchies; polymorphism, inheritance and dynamic binding; generic programming using templates, linked lists, queues, stacks, trees and algorithms such as searching, sorting, and hashing. Computational Engineering 322 and Statistics and Data Sciences 322 may not both be counted. Three lecture hours a week for one semester.
Pre/Corequisites: Aerospace Engineering 301 (or 201) or Computational Engineering 301 with a grade of at least C-.
Restrictions: Restricted to Computational Engineering majors.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
COE 129S, 229S, 329S, 429S, 529S, 629S, 729S, 829S, 929S Topics in Computational Engineering 1-9 Hours
1,2,3,4,5,6,7,8,9 Lecture Hours 0 Lab Hours
Used to record credit the student earns while enrolled at another institution in a program administered by the University's Study Abroad Office or the school's International Engineering Education Programs. Credit is recorded as assigned by the study abroad adviser in the Department of Aerospace Engineering and Engineering Mechanics. University credit is awarded for work in an exchange program; it may be counted as coursework taken in residence.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Upper Division
COE 332 Software Engineering and Design 3 Hours
3 Lecture Hours 0 Lab Hours
Covers methods and tools for planning, designing, implementing, validating and maintaining large software systems. May include project work to build a software system as a team, using appropriate software engineering tools and techniques. Three lecture hours a week for one semester.
Pre/Corequisites: Computational Engineering 322 with a grade of at least C-.
Restrictions: Restricted to computational engineering majors.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
COE 347 Introduction to Computational Fluid Dynamics 3 Hours
3 Lecture Hours 0 Lab Hours
Development and implementation of finite-difference schemes for numerical solution of subsonic, transonic, and supersonic flows. Emphasis on convection and diffusion equations of fluid dynamics. Evaluation of accuracy, stability, and efficiency. Only one of the following may be counted: Aerospace Engineering 347, Computational Engineering 347, Mechanical Engineering 369L. Three lecture hours a week for one semester.
Pre/Corequisites: Computational Engineering 311K (or Aerospace Engineering 211K or Computational Engineering 211K), and Aerospace Engineering 320 with a grade of at least C- in each.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
COE 352 Topics in Advanced Scientific Computations 3 Hours
3 Lecture Hours 0 Lab Hours
Topics in advanced numerical methods and scientific computation. Subject matter may vary. Three lecture hours a week for one semester.
Pre/Corequisites: Mathematics 427J or 427K, and Computational Engineering 322 (or Statistics and Data Sciences 322) with a grade of at least C- in each; and Computational Engineering 311K (or Computational Engineering 211K) with a grade of at least C-.
Restrictions: Restricted to computational engineering majors.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Upper Division
COE 362 Engineering Probability and Statistics 3 Hours
3 Lecture Hours 0 Lab Hours
Introduction to mathematical representations and computations to qualitatively and quantitatively describe data, compare data, ascribe distributions to apparent patterns and structure in data via inference methods. Describe and model discrete and continuous random variables. Aerospace Engineering 379L (Topic: Engr Probability and Stats) and Computational Engineering 362 may not both be counted. Three lecture hours a week for one semester.
Pre/Corequisites: Mathematics 408D, 408L, or 408M and Computational Engineering 311K with a grade of at least C- in each.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
COE 371 Applied Mathematics I 3 Hours
3 Lecture Hours 0 Lab Hours
Subjects include real analysis of functions of one variable, linear operator theory, and ordinary differential equations. Three lecture hours a week for one semester.
Pre/Corequisites: Upper-division standing; Mathematics 427J, 427L, and either Mathematics 362K or Computational Engineering 362 with a grade of at least C- in each.
Restrictions: Restricted to computational engineering majors.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
COE 372 Applied Mathematics II 3 Hours
3 Lecture Hours 0 Lab Hours
Subjects include elements of complex analysis, Fourier and Laplace transforms, partial differential equations, perturbation methods, analysis of functions of several variables. Three lecture hours per week for one semester.
Pre/Corequisites: Upper-division standing and Computational Engineering 371 with a grade of at least C-.
Restrictions: Restricted to computational engineering majors.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
COE 374 Senior Design Project 3 Hours
3 Lecture Hours 0 Lab Hours
Design projects from multiple engineering disciplines done with teams of students, using computational methods for analysis and experimental data for validation; ethics of design for safety and reliability; emphasis on written and oral reporting of engineering projects. Computational Engineering 374 and 374E may not both be counted. Three lecture hours a week for one semester.
Core Curriculum: 010
Pre/Corequisites: Computational Engineering 352 with a grade of at least C-; Computational Engineering 321K or Computational Engineering 347 with a grade of at least C-; and upper-division standing.
Restrictions: Restricted to computational engineering majors.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
COE 374D Computational Systems Senior Design I 3 Hours
3 Lecture Hours 4 Lab Hours
Introduction to computational systems engineering: the systems engineering process, requirements, design fundamentals, trade studies, cost and risk analyses, integration, technical reviews, case studies, and ethics. Produce written reports and oral presentations. Computational Engineering 374D and 379L (Topic: Senior Design I) may not both be counted Three lecture hours and four laboratory hours a week for one semester.
Pre/Corequisites: Computational Engineering 352 with a grade of at least C-; and Computational Engineering 321K or 347 with a grade of at least C-.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
COE 374E Computational Systems Senior Design II 3 Hours
3 Lecture Hours 4 Lab Hours
Produce design projects from multiple engineering disciplines done in teams, using computational methods for analysis and experimental data for validation; ethics of design for safety and reliability; and with emphasis on written and oral reporting of engineering projects. Computational Engineering 374 and 374E may not both be counted. Three lecture and four laboratory hours a week for one semester.
Core Curriculum: 010
Pre/Corequisites: Computational Engineering 374D with a grade of at least C-.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
COE 679H Undergraduate Honors Thesis 6 Hours
6 Lecture Hours 0 Lab Hours
Research performed during two consecutive semesters under the supervision of an engineering faculty member; topics are selected jointly by the student and the faculty member with approval by the director of the Engineering Honors Program. Entails an oral presentation and a written thesis. Individual instruction for two semesters.
Pre/Corequisites: For 679HA, enrollment in the Engineering Honors Program; for 679HB, Computational Engineering 679HA and enrollment in the Engineering Honors Program.
Restrictions: Restricted to computational engineering majors.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
COE 179K, 279K, 379K Research in Computational Engineering 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Directed study or research in a selected area of computational engineering. For each semester hour of credit earned, one lecture hour a week for one semester.
Pre/Corequisites: Upper-division standing, a University grade point average of at least 3.00, selection of project, and consent of the faculty member directing project and the undergraduate adviser.
Restrictions: Restricted to computational engineering majors.
Grading: Student Option
Repeatable for credit: Yes (Unlimited)
Academic Level: Upper Division
COE 179L, 279L, 379L, 479L Topics in Computational Engineering 1-4 Hours
1,2,3,4 Lecture Hours 0 Lab Hours
Current topics in computational engineering. For each semester hour of credit earned, the equivalent of one lecture hour a week for one semester.
Pre/Corequisites: Varies with the topic.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Upper Division
Topic 1: Introduction to Machine Learning and Data Sciences. Focus on algorithms, mathematics, implementations (on Scikit-learn), and real-life applications of machine learning and data sciences including machine learning algorithms such as supervised learnings (linear models, support vector machines, kernel methods, tree-based algorithms), unsupervised learnings (Density estimation, clustering, dimensionality reduction, PCA), and deep neural networks. Explore the foundations of machine learning including optimization, probability, advanced linear algebra, statistics, and methods to evaluate machine learning methods. Computational Engineering 379L (Topic: Intro to Machine Learning) and 379L (Topic 1) may not both be counted. Additional prerequisite: Computational Engineering 322, and Mathematics 427J or 340L with a grade of at least C- in each.
Topic 2: Simulation-based Aerodynamic Design and Analysis. Computational Engineering 379L (Topic: Simulatn Aerodynmc Dsgn/Anlys) and 379L (Topic 2) may not both be counted.
Topic 3: Computational Methods. Examine basics of vectors and matrices, linear equations, regression and classification, similarity measures, the Discrete Fourier Transform (DFT), linear filters, and power spectrum estimates. Focus on applying matrix methods to practical applications, such as tomography, image processing, data fitting, time series prediction, optimal control, finance, and machine learning. Utilize MATLAB or Python to do computations with vectors and matrices and run numerical experiments with real-world data sets. Computational Engineering 379L (Topic: Computational Methods) and 379L (Topic 3) may not both be counted. Additional prerequisite: Mathematics 408D, 408L, or 408M and Computational Engineering 301 with a grade of at least C- in each.
Topic 4: Fundamentals and Geophysical Applications of Imaging Radar Systems. Explore how radar images are formed and manipulated, as well as applications of the systems to problems such as measurement of the Earth crustal deformation. Focus on radar as a signal processing problem, radar image formation and radar interferometry. Discuss system design, scattering from natural surfaces, range and azimuth processing algorithms, and processor design. Computational Engineering 379L (Topic: Fund/Geophy App Imag Radar Sys) and 379L (Topic 4) may not both be counted.
Topic 5: Biomechanics of Tissues, Scaffolds, and Cells. Computational Engineering 379L (Topic: Tissue/Scaffold/Cell Biomch) and 379L (Topic 5) may not both be counted.
Topic 6: Software Design for Responsible Intelligent Systems. Discuss the design, implementation, operation, and assurance of intelligent software systems based on data intensive computing and machine learning techniques. Utilize real-world datasets from engineering disciplines. Computational Engineering 379L (Topic: Sw Dsgn For Resp Intellgnt Sys) and 379L (Topic 6) may not both be counted. Additional prerequisite: Computational Engineering 332 with a grade of at least C-.
Topic 7: Parallel Computing for Science and Engineering. Explore fundamentals of parallel programming for scientific and engineering applications. Examine the study of general parallel computing principles, including asymptotics; current computer architectures and how parallelism manifests itself in them. Computational Engineering 379L (Topic: Parallel Computng For Sci/Engr) and 379L (Topic 7) may not both be counted. Additional prerequisite: Mathematics 408M or 408D and Computational Engineering 322 with a grade of at least C-.
COE 179L.1, 279L.1, 379L.1, 479L.1 Introduction to Machine Learning and Data Sciences 1-4 Hours
1,2,3,4 Lecture Hours 0 Lab Hours
Focus on algorithms, mathematics, implementations (on Scikit-learn), and real-life applications of machine learning and data sciences including machine learning algorithms such as supervised learnings (linear models, support vector machines, kernel methods, tree-based algorithms), unsupervised learnings (Density estimation, clustering, dimensionality reduction, PCA), and deep neural networks. Explore the foundations of machine learning including optimization, probability, advanced linear algebra, statistics, and methods to evaluate machine learning methods. Computational Engineering 379L (Topic: Intro to Machine Learning) and 379L (Topic 1) may not both be counted.
Pre/Corequisites: Computational Engineering 322, and Mathematics 427J or 340L with a grade of at least C- in each.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
COE 179L.2, 279L.2, 379L.2, 479L.2 Simulation-based Aerodynamic Design and Analysis 1-4 Hours
1,2,3,4 Lecture Hours 0 Lab Hours
Computational Engineering 379L (Topic: Simulatn Aerodynmc Dsgn/Anlys) and 379L (Topic 2) may not both be counted.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
COE 179L.3, 279L.3, 379L.3, 479L.3 Computational Methods 1-4 Hours
1,2,3,4 Lecture Hours 0 Lab Hours
Examine basics of vectors and matrices, linear equations, regression and classification, similarity measures, the Discrete Fourier Transform (DFT), linear filters, and power spectrum estimates. Focus on applying matrix methods to practical applications, such as tomography, image processing, data fitting, time series prediction, optimal control, finance, and machine learning. Utilize MATLAB or Python to do computations with vectors and matrices and run numerical experiments with real-world data sets. Computational Engineering 379L (Topic: Computational Methods) and 379L (Topic 3) may not both be counted.
Pre/Corequisites: Mathematics 408D, 408L, or 408M and Computational Engineering 301 with a grade of at least C- in each.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
COE 179L.4, 279L.4, 379L.4, 479L.4 Fundamentals and Geophysical Applications of Imaging Radar Systems 1-4 Hours
1,2,3,4 Lecture Hours 0 Lab Hours
Explore how radar images are formed and manipulated, as well as applications of the systems to problems such as measurement of the Earth crustal deformation. Focus on radar as a signal processing problem, radar image formation and radar interferometry. Discuss system design, scattering from natural surfaces, range and azimuth processing algorithms, and processor design. Computational Engineering 379L (Topic: Fund/Geophy App Imag Radar Sys) and 379L (Topic 4) may not both be counted.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
COE 179L.5, 279L.5, 379L.5, 479L.5 Biomechanics of Tissues, Scaffolds, and Cells 1-4 Hours
1,2,3,4 Lecture Hours 0 Lab Hours
Computational Engineering 379L (Topic: Tissue/Scaffold/Cell Biomch) and 379L (Topic 5) may not both be counted.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
COE 179L.6, 279L.6, 379L.6, 479L.6 Software Design for Responsible Intelligent Systems 1-4 Hours
1,2,3,4 Lecture Hours 0 Lab Hours
Discuss the design, implementation, operation, and assurance of intelligent software systems based on data intensive computing and machine learning techniques. Utilize real-world datasets from engineering disciplines. Computational Engineering 379L (Topic: Sw Dsgn For Resp Intellgnt Sys) and 379L (Topic 6) may not both be counted.
Pre/Corequisites: Computational Engineering 332 with a grade of at least C-.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
COE 179L.7, 279L.7, 379L.7, 479L.7 Parallel Computing for Science and Engineering 1-4 Hours
1,2,3,4 Lecture Hours 0 Lab Hours
Explore fundamentals of parallel programming for scientific and engineering applications. Examine the study of general parallel computing principles, including asymptotics; current computer architectures and how parallelism manifests itself in them. Computational Engineering 379L (Topic: Parallel Computng For Sci/Engr) and 379L (Topic 7) may not both be counted.
Pre/Corequisites: Mathematics 408M or 408D and Computational Engineering 322 with a grade of at least C-.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division